Weights & Biases
Track ML experiments, log metrics, compare training runs, and manage models from training to production. The platform data science teams use to stop re-running experiments they've already done.
Overview
Weights & Biases is the MLOps platform that tracks every training run, logs metrics, compares experiments, visualizes model behavior, and manages the full ML lifecycle — from training to deployment to monitoring in production. Data scientists and ML engineers use it to answer 'why did this run perform better than that one' without digging through logs, to collaborate on experiment results across the team, and to build dashboards that show model performance over time. The recent LLM features add prompt management, evaluation datasets, and trace logging for production LLM applications.
Key Features
- Experiment tracking
- Metric logging and visualization
- Model versioning
- Prompt management
- Dataset versioning
- Production monitoring
- • Experiment comparison eliminates the 'which run was that' problem permanently
- • Industry standard — integrations with every major ML framework
- • LLM features extend the platform to production application monitoring
- • Can be overkill for simple fine-tuning jobs or prompt engineering projects
- • Team pricing adds up quickly for large ML organizations